Predictive modelling, how to build ground-truth and extract features for action prediction? • /r/MachineLearning

I have a dataset of users, each user has has daily information about his activities (numerical values representing some measurements of his physical activities). In addition, each user in each day has a boolean value that represents if he/she took a particular action. The data set is not fixed, so new activities information and action are added for each user each new day. Build a model that predicts which user is likely to take the action in the near future (e.g. in any of the next 7 days). My approach is to build feature vectors representing the activity values for each users over a period of time, and use the action column as a source of ground-truth.